Sr. Data Engineer

BISSELL Homecare•Grand Rapids, MI

About The Position

The Sr. Data Engineer will serve as a key technical contributor responsible for the design, development, and ongoing support of the company's enterprise data platform and business intelligence ecosystem. This role enables the organization to extract hidden insights from vast amounts of data—leveraging scalable ETL pipelines and cloud technologies—to empower smarter, faster decision-making across the business. This person will lead the development of scalable ETL/ELT pipelines using IICS (Informatica Intelligent Cloud Services) and AWS services (Lambda, Glue, Redshift, Bedrock, and SageMaker). Working closely with Data Engineers, BI analysts, business stakeholders, and external consultants, this person will architect and evolve the data lake—ensuring data quality and integrity are maintained at all times. The Sr. Data Engineer must be a strong problem solver and multitasker who thrives managing multiple concurrent projects, resolves production bugs under pressure, and translates complex business needs into scalable technical solutions.

Requirements

6+ years of hands-on experience in data engineering and ETL/ELT development is required.
Strong proficiency in Python and GUI ETL tools such as IICS (Informatica Intelligent Cloud Services)
Experience with AWS data services (Lambda, Glue, Redshift, Bedrock, SageMaker).
Strong Python proficiency: ability to develop, analyze, debug, and optimize ETL pipelines, automation scripts, and data transformation logic end-to-end.
Hands-on experience with IICS (Informatica Intelligent Cloud Services) for enterprise-scale ETL/ELT development, maintenance, and CI/CD pipeline integration.
Deep AWS expertise across Lambda, Glue, Redshift, Bedrock, and SageMaker; ability to architect and deploy cloud-native data solutions.
Experience working with generative AI services such as AWS Bedrock or SageMaker to support AI-powered data workflows and analytics use cases.
Proven ability to resolve bugs and incidents in production data pipelines; conducts root-cause analysis and implements preventive controls to maintain SLA commitments.
Must be a strong multitasker with the ability to manage and lean into multiple concurrent projects simultaneously; quickly and accurately shifts attention among dynamic priorities, picks up new initiatives with minimal ramp-up time, and delivers results across workstreams without sacrificing quality or missing deadlines.
Strong critical-thinking and problem-solving skills: demonstrates a decisive, creative approach to difficult business and technical challenges; translates problems into practical, scalable solutions.
Ability to develop, analyze, and modify complex processes, data structures, and models.
Ability to create and maintain thorough documentation, including requirements, functional/technical designs, and user manuals.
Excellent listening, communication, interpersonal, and presentation skills; ability to work with and interview management, consultants, and subject matter experts to gather, identify, and document complex business requirements.
Organizational ability: demonstrates a systematic, process-oriented approach; excels at cutting through ambiguity and turning chaos into order across large, multi-stakeholder projects.
Client service skills: consistently provides attentive, courteous, and informed service; willing to listen and collaborate with customers; driven to deliver practical, sustainable results.
Quality control mindset: checks data pipelines, new data sets, and reports for errors related to both data accuracy and presentation.
Self-motivated, independent, and resourceful; able to plan, organize, and execute projects with minimal supervision.
Effective in a team-oriented environment; works collaboratively with data engineers, BI analysts, business stakeholders, and external consultants.
This position requires the ability to manage data flows, perform analysis, generate summary reports, and communicate by voice or data while connected from the office, at home, or while traveling by laptop or mobile device regularly without regard to time zone or day of the week.

Responsibilities

Lead the design and development of ETL/ELT code to integrate data into the enterprise data lake using IICSd (Informatica Intelligent Cloud Services) and AWS Glue; mentor junior engineers and contribute to platform architecture decisions with direction from the Data Engineering Lead.
Develop and maintain deep knowledge of the customer's business needs as they relate to data flows, data models, and BI consumption patterns.
Support the integration of AI and machine learning capabilities into data workflows using AWS Bedrock and SageMaker; ensure data pipelines are clean, well-structured, and optimized for analytics and AI consumption.
Design and implement AI-ready data pipelines—ensuring data is clean, well-structured, and optimized for downstream model training, inference, and analytics consumption.
Collaborate with business process owners, external consultants, and the broader business community to define, understand, and prioritize demand for process and solution changes that optimize business value and BI delivery efficiency.
Work closely with the Data Engineering Lead to estimate levels of effort for platform changes and partner with the business community to set priorities across multiple concurrent initiatives.
Own the full delivery lifecycle: participate in requirements gathering; prepare requirements documents, functional and technical designs, test scenarios, and deployment plans.
Manage and resolve all Data Engineering production support tickets; proactively triage and fix bugs in production pipelines, maintain SLAs, and report resolution progress to the business as requested.
Leverage AWS services—including Lambda, Glue, Redshift, Bedrock, and SageMaker—to build, optimize, and modernize data platform capabilities.
Understand where data originates and how it is used across most process areas; ensure data lineage, quality, and integrity are maintained throughout the data lifecycle.
Champion continuous improvement across data engineering practices, tooling, and documentation standards.
May be required to perform other duties as assigned.